Uterine cancer is the 4th most common malignancy in women and the 5th leading cause of cancer-related mortality.¹ Uterine cancer is one of the only tumors in which the incidence and death rate are increasing. From 1999-2015, the incidence rate rose 0.7% per year while the death rate increased by 1.1% per year.² This rising mortality rate from uterine cancer is greater than for any other cancer, in either men or women.³ Despite the growing burden of uterine cancer in the U.S., the disease remains understudied. There are currently no consensus recommendations for routine screening with either commonly available tests or new technologies, even among high-risk women.

The CISNET Uterine group consists of three independent uterine cancer models: The Columbia University Uterine Cancer Model (CU-UTMO), The Duke University Uterine Cancer Model (DU-CAM), and the Mt. Sinai Uterine Cancer Model (MUSIC) (Figure 1).

Structure of the CISNET Uterine Group

Figure 1: The CISNET Gastric group is composed of three gastric cancer models: the Columbia University Gastric Cancer Simulation Model (GSiMo), the Harvard and Stanford University Gastric Cancer Model, and the Erasmus University Medical Center Microsimulation Screening Analysis Gastric Cancer Model (MISCAN-Gastric).

Common structures and inputs

All three uterine cancer models include a natural history component incorporating known risk factors for uterine cancer and include age-period-cohort (APC) effects. While modeling methods differ, all three models are predicated on factors that are clinically and epidemiologically central to uterine cancer including: 1) inclusion of obesity and hysterectomy trends across age, period, and cohort and 2) inclusion of endometrioid uterine cancer and more aggressive, non-endometrioid and sarcoma histologic subtypes. The CU-UTMO and MUSIC models are microsimulation models and use monthly cycles, while the DU-CAM model is a multistage clonal expansion model and uses annual cycles. All models use SEER data for incidence and mortality calibration targets. While all models incorporate birth cohorts, CU-UTMO uses 10-year cohorts from 1910-1920 to 1990-2000, MUSIC uses 10-year birth cohorts from 1910-1920 to 2010-2020, and DU-CAM uses single year birth cohorts from 1915-2000 spanning years 2000-2020. The starting population age is 18 years for CU-UTMO, 20 years for MUSIC, and from birth for DU-CAM. All three models currently include Non-Hispanic White and Non-Hispanic Black females.

For natural history, all 3 models include a precancer state, endometrial intraepithelial neoplasia (EIN). Recurrence is captured in all models and explicitly modeled in MUSIC and DU-CAM. All three models include AJCC stage in their natural history models. All models incorporate obesity as a risk factor in their models and DU-CAM also incorporates reproductive history. The models incorporate common inputs for obesity with the obesity history generator developed by the DU-CAM group, hysterectomy using NHANES or BRFSS data, competing mortality using CDC Wonder data, and survival using SEER or SEER-Medicare data.

Recent studies

The Uterine CISNET group has several recent and pending publications. We published a study on the contribution of age, period, and cohort effects to the uterine cancer incidence rate and observed differences by histology.⁴ We developed a decision-tree model to compare ultrasound-based versus immediate biopsy-based management of postmenopausal bleeding, the most common uterine cancer symptom, and observed that the immediate biopsy-based strategy had higher value than the ultrasound-based strategy.⁵ In another study, we examined trends in the use of weight-loss therapy for patients with endometrial intraepithelial neoplasia (EIN) and uterine cancer.⁶ We found that the use of weight-loss therapy after diagnosis has increased over time for patients with EIN and uterine cancer and were more likely in younger patients and patients with comorbidities.

Base case modeling papers for CU-UTMO⁷ and DU-CAM⁸ are pending publication. The CU-UTMO base case paper explores uterine cancer incidence and mortality trends and projections, as well as projections of obesity and hysterectomy. We projected increases in both uterine cancer incidence and mortality over the next three decades in the absence of changes in risk factors, diagnosis, and treatment. The DU-CAM base case paper explores the contribution of reproductive histories and body mass index (BMI) to uterine cancer incidence. We found that reproductive histories and BMI substantially contribute to uterine cancer incidence and lower rates of hysterectomy and rising obesity rates contribute to the continued increase in uterine cancer incidence. Lastly, we are developing our first comparative modeling paper which includes projected incidence and mortality trends in uterine cancer and are finalizing a systematic review and meta-analysis on uterine cancer screening and early detection techniques which will inform future analyses focused on identifying optimal screening and detection strategies for uterine cancer across the risk spectrum.

Impact

We anticipate that this work will be imminently actionable for patients, providers and policy makers. As funding and cancer control initiatives for uterine cancer have lagged, the findings of this work can be quickly utilized to develop strategies for screening, detection, and prevention using widely available and emerging tests and therapies. These data will provide estimates of the effectiveness and cost-effectiveness of these strategies for populations at varying risk. Similarly, this work will help inform treatment decision making for women with newly diagnosed, early stage or metastatic uterine cancer, for adjuvant therapy, and for treatment of recurrent disease. Our models will provide precision estimates weighing harms, benefits, and costs for patients based on disease characteristics as well as underlying individual characteristics such as age and co-morbidities.

References

American Cancer Society. Cancer Facts & Figures 2024. Atlanta: American Cancer Society; 2024.
Henley SJ, Miller JW, Dowling NF, Benard VB, Richardson LC. Uterine Cancer Incidence and Mortality - United States, 1999-2016. MMWR Morb Mortal Wkly Rep 2018;67:1333-8.
Cronin KA, Scott S, Firth AU, Sung H, Henley SJ, Sherman RL, Siegel RL, Anderson RN, Kohler BA, Benard VB, Negoita S, Wiggins C, Cance WG, Jemal A. Annual report to the nation on the status of cancer, part 1:National cancer statistics. Annual Report: National Cancer Statistics. 2022; 128: 4251-4284.
Ferris JS, Prest MT, Hur C, Chen L, Elkin EB, Melamed A, Kong CY, Myers ER, Havrilesky LJ, Blank, SV, Hazelton WD, Wright JD. Trends in uterine cancer incidence in the United States: The contribution of age, period, and cohort effects. Gynecologic Oncology. 2024; 187:151-162.
Nolin AC, Atkins SL, Myers ER, Wentzensen N, Clarke MA, Blank SV, Wright JD, Doll KM, Havrilesky LJ. Ultrasound-based versus immediate biopsy-based management of postmenopausal bleeding in non-Hispanic Black and non-Hispanic White individuals. Gynecologic Oncology. 2025; 194: 105-111.
Suzuki Y, Chen L, Matsuo K, Ferris JS, Elkin EB, Melamed A, Kong CY, Bickell N, Myers ER, Havrilesky LJ, Xu X, Blank SV, Hazelton WD, Hershman DL, Wright JD. Weight-loss therapy in patients with obesity with endometrial intraepithelial neoplasia and uterine cancer. Gynecologic Oncology. 2025; 190: 78-83.
Wright JD, Prest MT, Ferris JS, Chen L, Xu X, Rouse KJ, Melamed A, Hur C, Stoddard-Heckman B, Samimi G, Bickell N, Layne T, Myers ER, Havrilesky LJ, Blank SV, Stout N, Hazelton WD, Kong CY, Elkin EB. Projected Trends in the Incidence and Mortality of Uterine Cancer. Cancer Epidemiology, Biomarkers, and Prevention. 2025. Pending publication.
Hazelton WD, Prest MT, Chen L, Rouse KJ, Elkin EB, Ferris JS, Xu X, Bickell N, Kong CY, Blank SV, Feuer EJ, Samimi G, Stoddard-Heckman B, Layne T, Wright JD, Myers ER, Havrilesky LJ. Modeling the impact of obesity, reproductive history, and hysterectomy on US uterine cancer trends. Journal of the National Cancer Institute. 2025. Pending publication.